Now showing 1 - 10 of 674
  • Publication
    Holistic Approach for Digitalized Quality Assurance in Battery Cell Production
    In this paper, we introduce a holistic approach to consider quality assurance (QA) for battery cell production (BCP). The framework, the explanation of the individual components as well as their interfaces and dependencies, and a detailed description are presented. Firstly, the level of necessary data (e. g. provided by online and out-of-line measurement systems) for the inspection of quality is presented. The aggregation of the recorded data as well as their tracing are ensured by the realization of a traceability system. Subsequently, by defining a suitable intelligent quality gate system, QA mechanisms are implemented and an active influence on production - e. g. by adaptive process control or identifying and reducing negative influence of cause-effect relationships - is aimed at. Finally, optimization of BCP in terms of product quality and its sustainability will be enabled. The evaluation of the demonstrated approach in practice is outlined based on an exemplary process of BCP.
  • Publication
    Wie Machine Learning auf dem Shopfloor die Produktionsqualität steigert. Intelligente Qualitätsplattform
    Mit der intelligenten Qualitätsplattform (IQP) wurde am Fraunhofer-Institut für Produktionstechnologie IPT eine Management-Plattform zur Nutzung, Überwachung und fortlaufenden Optimierung verschiedener ML-Anwendungen entwickelt. Dazu zählen prädiktive Wartung von Maschinen, Vorhersage der Produktqualität oder das Erkennen von Maschinenauffälligkeiten. Die IQP dient dazu, verschiedene ML-Anwendungen aus unterschiedlichen Produktionsbereichen standardisiert zu integrieren und parallel zu betreiben.
  • Publication
    Machine learning pipeline for application in manufacturing
    The integration of machine learning (ML) into manufacturing processes is crucial for optimizing efficiency, reducing costs, and enhancing overall productivity. This paper proposes a comprehensive ML pipeline tailored for manufacturing applications, leveraging the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) as its foundational framework. The proposed pipeline consists of key phases, namely business understanding, use case selection and specification, data integration, data preparation, modelling, deployment, and certification. These are designed to meet the unique requirements and challenges associated with ML implementation in manufacturing settings. Within each phase, sub-topics are defined to provide a granular understanding of the workflow. Responsibilities are clearly outlined to ensure a structured and efficient execution, promoting collaboration among stakeholders. Further, the input and output of each phase are defined. The methodology outlined in this research not only enhances the applicability of CRISP-DM in the manufacturing domain but also serves as a guide for practitioners seeking to implement ML solutions in a systematic and well-defined manner. The proposed pipeline aims to streamline the integration of ML technologies into manufacturing processes, facilitating informed decision-making and fostering the development of intelligent and adaptive manufacturing systems.
  • Publication
    ICNAP Study Report 2023
    In 2023, we continued our close collaboration with the community through studies and community events. At ICNAP, we believe that strong cooperation is the key to unlocking the full potential of networked, adaptive production. We aim to foster meaningful collaboration between research partners, manufacturing companies, and digital enablers. ICNAP serves as an ideal platform for networking and collaboration in this field. We have an active community of members and are constantly tackling new challenges. In this report, we would like to share the five studies we conducted in 2023. These studies cover a range of topics including industrializing artificial intelligence, innovative power solutions, realizing plug-and-produce, a digital twin demonstrator, and an energy monitoring framework. Each study was selected through an exclusive voting process involving all community members, ensuring their relevance to the industry and the needs of the ICNAP community. We hope this report provides insight into the current state of networked, adaptive production and the work carried out at ICNAP. For more information about these studies or our community, please visit www.icnap.de.
  • Publication
    The Digital Twin Demonstrator
    ( 2023-11-27)
    Bäckel, Niklas
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    Gilerson, Andre
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  • Publication
    Digital transformation of CAR-T cell therapy - challenges and potential for Industry 4.0
    ( 2023-10-26) ;
    Sanges, Carmen
    ;
    Jacobs, John J.L.
    ;
    Hudecek, Michael
    ;
    With the approval of CAR-T cell therapy as a treatment for acute leukemia and lymphoma in 2018, the first advanced therapy medicinal product (ATMP) came onto the market. ATMPs are cell- and gene-based products and very promising therapies for the successful treatment of various hereditary diseases and cancers. In recent years, more ATMPs have been approved and a strong increase is expected considering current clinical trial numbers. However, CAR-T cell production still poses great challenges. The CAR-T treatment is very costly for approved products (approx. 400.000 $). Most of the associated costs are due to the expensive manufacturing processes. Furthermore, autologous CAR-T cell therapy is limited in its scalability by a patient-specific make-to-order approach which makes economic production difficult. To make CAR-T cell products and other ATMP available to a large number of patients, the complex multi-step manufacturing process must be further developed and the potential for automation and digitalization exploited. Therefore, this article provides an analysis of CAR-T cell production focusing on characteristics, challenges, and optimization potential. For strategic guidance in the digital transformation, a systematic approach, based on the Industry 4.0 maturity index, is introduced categorizing challenges and use cases into six stages.